This paper proposed a modified version of whale optimization algorithm (MVWOA), which incorporates four critical components. Firstly, a dynamic reverse learning strategy is designed to improve the uniformity of the initial population with better location in the optimization process. Secondly, to mitigate premature convergence, a Lévy flight mechanism with an optimized pa factor is proposed to increase population diversity over the course of iterations. Thirdly, an information acquisition and sharing strategy facilitates inter-agent communication within the population for accelerating the convergence rate. Finally, a differential evolution strategy is integrated to improve the efficiency of approaching the obtained solutions. Besides that, unmanned aerial vehicle (UAV) path planning models in two-dimensional and three-dimensional are also bulit to measure the proposed algorithm in practical. The performance of MVWOA is evaluated on the CEC2022 benchmark set as well as UAV path planning problems. Simulation results indicate that the proposed algorithm outperforms some state-of-the-art algorithms considering on accuracy and convergence, demonstrating its potential for solving applications.
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